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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.register_extension_type.html">pyarrow.register_extension_type</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_boolean.html">pyarrow.types.is_boolean</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_signed_integer.html">pyarrow.types.is_signed_integer</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_unsigned_integer.html">pyarrow.types.is_unsigned_integer</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_int8.html">pyarrow.types.is_int8</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_uint32.html">pyarrow.types.is_uint32</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_uint64.html">pyarrow.types.is_uint64</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_floating.html">pyarrow.types.is_floating</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_float16.html">pyarrow.types.is_float16</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_float32.html">pyarrow.types.is_float32</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_float64.html">pyarrow.types.is_float64</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_date32.html">pyarrow.types.is_date32</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_date64.html">pyarrow.types.is_date64</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_time.html">pyarrow.types.is_time</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_time32.html">pyarrow.types.is_time32</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_time64.html">pyarrow.types.is_time64</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_duration.html">pyarrow.types.is_duration</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_interval.html">pyarrow.types.is_interval</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_large_binary.html">pyarrow.types.is_large_binary</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_dictionary.html">pyarrow.types.is_dictionary</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.is_primitive.html">pyarrow.types.is_primitive</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.types.TypesEnum.html">pyarrow.types.TypesEnum</a></li> |
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| </details></li> |
| <li class="toctree-l3 has-children"><a class="reference internal" href="../api/arrays.html">Arrays and Scalars</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.array.html">pyarrow.array</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.IntegerArray.html">pyarrow.IntegerArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.Int8Array.html">pyarrow.Int8Array</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.Int16Array.html">pyarrow.Int16Array</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.Int32Array.html">pyarrow.Int32Array</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.Int64Array.html">pyarrow.Int64Array</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.NullArray.html">pyarrow.NullArray</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.UInt8Array.html">pyarrow.UInt8Array</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.HalfFloatArray.html">pyarrow.HalfFloatArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.FloatArray.html">pyarrow.FloatArray</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.StringArray.html">pyarrow.StringArray</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.TimestampArray.html">pyarrow.TimestampArray</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.MonthDayNanoIntervalArray.html">pyarrow.MonthDayNanoIntervalArray</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ListArray.html">pyarrow.ListArray</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.MapArray.html">pyarrow.MapArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.RunEndEncodedArray.html">pyarrow.RunEndEncodedArray</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.UnionArray.html">pyarrow.UnionArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ExtensionArray.html">pyarrow.ExtensionArray</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.Bool8Array.html">pyarrow.Bool8Array</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.scalar.html">pyarrow.scalar</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.Scalar.html">pyarrow.Scalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.BooleanScalar.html">pyarrow.BooleanScalar</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.NullScalar.html">pyarrow.NullScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.UInt8Scalar.html">pyarrow.UInt8Scalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.UInt16Scalar.html">pyarrow.UInt16Scalar</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.FloatScalar.html">pyarrow.FloatScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.DoubleScalar.html">pyarrow.DoubleScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.BinaryScalar.html">pyarrow.BinaryScalar</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.LargeBinaryScalar.html">pyarrow.LargeBinaryScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.LargeStringScalar.html">pyarrow.LargeStringScalar</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.StringViewScalar.html">pyarrow.StringViewScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.Time32Scalar.html">pyarrow.Time32Scalar</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.DurationScalar.html">pyarrow.DurationScalar</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.Decimal256Scalar.html">pyarrow.Decimal256Scalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.DictionaryScalar.html">pyarrow.DictionaryScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.RunEndEncodedScalar.html">pyarrow.RunEndEncodedScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ListScalar.html">pyarrow.ListScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.FixedSizeListScalar.html">pyarrow.FixedSizeListScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.LargeListScalar.html">pyarrow.LargeListScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ListViewScalar.html">pyarrow.ListViewScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.LargeListViewScalar.html">pyarrow.LargeListViewScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.MapScalar.html">pyarrow.MapScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.StructScalar.html">pyarrow.StructScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.UnionScalar.html">pyarrow.UnionScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ExtensionScalar.html">pyarrow.ExtensionScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.FixedShapeTensorScalar.html">pyarrow.FixedShapeTensorScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.OpaqueScalar.html">pyarrow.OpaqueScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.JsonScalar.html">pyarrow.JsonScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.UuidScalar.html">pyarrow.UuidScalar</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.Bool8Scalar.html">pyarrow.Bool8Scalar</a></li> |
| </ul> |
| </details></li> |
| <li class="toctree-l3 has-children"><a class="reference internal" href="../api/memory.html">Buffers and Memory</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.allocate_buffer.html">pyarrow.allocate_buffer</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.py_buffer.html">pyarrow.py_buffer</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.foreign_buffer.html">pyarrow.foreign_buffer</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.Buffer.html">pyarrow.Buffer</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ResizableBuffer.html">pyarrow.ResizableBuffer</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.Codec.html">pyarrow.Codec</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.compress.html">pyarrow.compress</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.decompress.html">pyarrow.decompress</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.MemoryPool.html">pyarrow.MemoryPool</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.default_memory_pool.html">pyarrow.default_memory_pool</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.jemalloc_memory_pool.html">pyarrow.jemalloc_memory_pool</a></li> |
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| </details></li> |
| <li class="toctree-l3 has-children"><a class="reference internal" href="../api/acero.html">Acero - Streaming Execution Engine</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul> |
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| </ul> |
| </details></li> |
| <li class="toctree-l3 has-children"><a class="reference internal" href="../api/substrait.html">Substrait</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.substrait.serialize_expressions.html">pyarrow.substrait.serialize_expressions</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.substrait.serialize_schema.html">pyarrow.substrait.serialize_schema</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.substrait.deserialize_schema.html">pyarrow.substrait.deserialize_schema</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.substrait.SubstraitSchema.html">pyarrow.substrait.SubstraitSchema</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.substrait.get_supported_functions.html">pyarrow.substrait.get_supported_functions</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.create_memory_map.html">pyarrow.create_memory_map</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.PythonFile.html">pyarrow.PythonFile</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.BufferReader.html">pyarrow.BufferReader</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.CompressedOutputStream.html">pyarrow.CompressedOutputStream</a></li> |
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| <li class="toctree-l3 has-children"><a class="reference internal" href="../api/ipc.html">Serialization and IPC</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.new_file.html">pyarrow.ipc.new_file</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.open_file.html">pyarrow.ipc.open_file</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.new_stream.html">pyarrow.ipc.new_stream</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.open_stream.html">pyarrow.ipc.open_stream</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.read_message.html">pyarrow.ipc.read_message</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.read_record_batch.html">pyarrow.ipc.read_record_batch</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.get_record_batch_size.html">pyarrow.ipc.get_record_batch_size</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.read_tensor.html">pyarrow.ipc.read_tensor</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.write_tensor.html">pyarrow.ipc.write_tensor</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.get_tensor_size.html">pyarrow.ipc.get_tensor_size</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.IpcWriteOptions.html">pyarrow.ipc.IpcWriteOptions</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.MessageReader.html">pyarrow.ipc.MessageReader</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.RecordBatchFileReader.html">pyarrow.ipc.RecordBatchFileReader</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.RecordBatchFileWriter.html">pyarrow.ipc.RecordBatchFileWriter</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.RecordBatchStreamReader.html">pyarrow.ipc.RecordBatchStreamReader</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.ipc.RecordBatchStreamWriter.html">pyarrow.ipc.RecordBatchStreamWriter</a></li> |
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| <li class="toctree-l3 has-children"><a class="reference internal" href="../api/flight.html">Arrow Flight</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.flight.FlightStreamWriter.html">pyarrow.flight.FlightStreamWriter</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.flight.GeneratorStream.html">pyarrow.flight.GeneratorStream</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.flight.RecordBatchStream.html">pyarrow.flight.RecordBatchStream</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.flight.ServerCallContext.html">pyarrow.flight.ServerCallContext</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.flight.ServerMiddlewareFactory.html">pyarrow.flight.ServerMiddlewareFactory</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.flight.ServerMiddleware.html">pyarrow.flight.ServerMiddleware</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.flight.FlightError.html">pyarrow.flight.FlightError</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.flight.FlightCancelledError.html">pyarrow.flight.FlightCancelledError</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.flight.FlightServerError.html">pyarrow.flight.FlightServerError</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.flight.FlightTimedOutError.html">pyarrow.flight.FlightTimedOutError</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.flight.FlightMethod.html">pyarrow.flight.FlightMethod</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.flight.CallInfo.html">pyarrow.flight.CallInfo</a></li> |
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| <li class="toctree-l3 has-children"><a class="reference internal" href="../api/formats.html">Tabular File Formats</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.csv.ISO8601.html">pyarrow.csv.ISO8601</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.csv.open_csv.html">pyarrow.csv.open_csv</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.csv.read_csv.html">pyarrow.csv.read_csv</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.csv.write_csv.html">pyarrow.csv.write_csv</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.csv.InvalidRow.html">pyarrow.csv.InvalidRow</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.feather.read_feather.html">pyarrow.feather.read_feather</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.feather.read_table.html">pyarrow.feather.read_table</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.feather.write_feather.html">pyarrow.feather.write_feather</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.json.ReadOptions.html">pyarrow.json.ReadOptions</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.json.ParseOptions.html">pyarrow.json.ParseOptions</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.json.read_json.html">pyarrow.json.read_json</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.parquet.ParquetFile.html">pyarrow.parquet.ParquetFile</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.parquet.ParquetWriter.html">pyarrow.parquet.ParquetWriter</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.parquet.read_table.html">pyarrow.parquet.read_table</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.parquet.read_metadata.html">pyarrow.parquet.read_metadata</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.parquet.read_pandas.html">pyarrow.parquet.read_pandas</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.parquet.read_schema.html">pyarrow.parquet.read_schema</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.parquet.write_metadata.html">pyarrow.parquet.write_metadata</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.parquet.write_to_dataset.html">pyarrow.parquet.write_to_dataset</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.parquet.ColumnSchema.html">pyarrow.parquet.ColumnSchema</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.parquet.ParquetLogicalType.html">pyarrow.parquet.ParquetLogicalType</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.parquet.encryption.EncryptionConfiguration.html">pyarrow.parquet.encryption.EncryptionConfiguration</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.orc.ORCFile.html">pyarrow.orc.ORCFile</a></li> |
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| <li class="toctree-l3 has-children"><a class="reference internal" href="../api/filesystems.html">Filesystems</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.fs.FileSystem.html">pyarrow.fs.FileSystem</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.fs.GcsFileSystem.html">pyarrow.fs.GcsFileSystem</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.fs.HadoopFileSystem.html">pyarrow.fs.HadoopFileSystem</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.fs.SubTreeFileSystem.html">pyarrow.fs.SubTreeFileSystem</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.fs.AzureFileSystem.html">pyarrow.fs.AzureFileSystem</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.fs.PyFileSystem.html">pyarrow.fs.PyFileSystem</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.fs.FileSystemHandler.html">pyarrow.fs.FileSystemHandler</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.fs.FSSpecHandler.html">pyarrow.fs.FSSpecHandler</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.fs.copy_files.html">pyarrow.fs.copy_files</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.fs.initialize_s3.html">pyarrow.fs.initialize_s3</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.fs.finalize_s3.html">pyarrow.fs.finalize_s3</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.fs.resolve_s3_region.html">pyarrow.fs.resolve_s3_region</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.fs.S3LogLevel.html">pyarrow.fs.S3LogLevel</a></li> |
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| <li class="toctree-l3 has-children"><a class="reference internal" href="../api/dataset.html">Dataset</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="pyarrow.dataset.PartitioningFactory.html">pyarrow.dataset.PartitioningFactory</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="pyarrow.dataset.DirectoryPartitioning.html">pyarrow.dataset.DirectoryPartitioning</a></li> |
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| <section id="pyarrow-recordbatch"> |
| <h1>pyarrow.RecordBatch<a class="headerlink" href="#pyarrow-recordbatch" title="Link to this heading">#</a></h1> |
| <dl class="py class"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch"> |
| <em class="property"><span class="k"><span class="pre">class</span></span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyarrow.</span></span><span class="sig-name descname"><span class="pre">RecordBatch</span></span><a class="headerlink" href="#pyarrow.RecordBatch" title="Link to this definition">#</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">_Tabular</span></code></p> |
| <p>Batch of rows of columns of equal length</p> |
| <div class="admonition warning"> |
| <p class="admonition-title">Warning</p> |
| <p>Do not call this class’s constructor directly, use one of the |
| <code class="docutils literal notranslate"><span class="pre">RecordBatch.from_*</span></code> functions instead.</p> |
| </div> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">]</span> |
| </pre></div> |
| </div> |
| <p>Constructing a RecordBatch from arrays:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="n">names</span><span class="p">)</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [2,2,4,4,5,100]</span> |
| <span class="go">animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="n">names</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animals</span> |
| <span class="go">0 2 Flamingo</span> |
| <span class="go">1 2 Parrot</span> |
| <span class="go">2 4 Dog</span> |
| <span class="go">3 4 Horse</span> |
| <span class="go">4 5 Brittle stars</span> |
| <span class="go">5 100 Centipede</span> |
| </pre></div> |
| </div> |
| <p>Constructing a RecordBatch from pandas DataFrame:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2021</span><span class="p">,</span> <span class="mi">2022</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'month'</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'day'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">13</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">year: int64</span> |
| <span class="go">month: int64</span> |
| <span class="go">day: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [2020,2022,2021,2022]</span> |
| <span class="go">month: [3,5,7,9]</span> |
| <span class="go">day: [1,5,9,13]</span> |
| <span class="go">n_legs: [2,4,5,100]</span> |
| <span class="go">animals: ["Flamingo","Horse","Brittle stars","Centipede"]</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> year month day n_legs animals</span> |
| <span class="go">0 2020 3 1 2 Flamingo</span> |
| <span class="go">1 2022 5 5 4 Horse</span> |
| <span class="go">2 2021 7 9 5 Brittle stars</span> |
| <span class="go">3 2022 9 13 100 Centipede</span> |
| </pre></div> |
| </div> |
| <p>Constructing a RecordBatch from pylist:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pylist</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">:</span> <span class="s1">'Flamingo'</span><span class="p">},</span> |
| <span class="gp">... </span> <span class="p">{</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">:</span> <span class="s1">'Dog'</span><span class="p">}]</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_pylist</span><span class="p">(</span><span class="n">pylist</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animals</span> |
| <span class="go">0 2 Flamingo</span> |
| <span class="go">1 4 Dog</span> |
| </pre></div> |
| </div> |
| <p>You can also construct a RecordBatch using <a class="reference internal" href="pyarrow.record_batch.html#pyarrow.record_batch" title="pyarrow.record_batch"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.record_batch()</span></code></a>:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">record_batch</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="n">names</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animals</span> |
| <span class="go">0 2 Flamingo</span> |
| <span class="go">1 2 Parrot</span> |
| <span class="go">2 4 Dog</span> |
| <span class="go">3 4 Horse</span> |
| <span class="go">4 5 Brittle stars</span> |
| <span class="go">5 100 Centipede</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">record_batch</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">year: int64</span> |
| <span class="go">month: int64</span> |
| <span class="go">day: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [2020,2022,2021,2022]</span> |
| <span class="go">month: [3,5,7,9]</span> |
| <span class="go">day: [1,5,9,13]</span> |
| <span class="go">n_legs: [2,4,5,100]</span> |
| <span class="go">animals: ["Flamingo","Horse","Brittle stars","Centipede"]</span> |
| </pre></div> |
| </div> |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.__init__"> |
| <span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.__init__" title="Link to this definition">#</a></dt> |
| <dd></dd></dl> |
| |
| <p class="rubric">Methods</p> |
| <div class="pst-scrollable-table-container"><table class="autosummary longtable table"> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.__init__" title="pyarrow.RecordBatch.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(*args, **kwargs)</p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.add_column" title="pyarrow.RecordBatch.add_column"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_column</span></code></a>(self, int i, field_, column)</p></td> |
| <td><p>Add column to RecordBatch at position i.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.append_column" title="pyarrow.RecordBatch.append_column"><code class="xref py py-obj docutils literal notranslate"><span class="pre">append_column</span></code></a>(self, field_, column)</p></td> |
| <td><p>Append column at end of columns.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.cast" title="pyarrow.RecordBatch.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(self, Schema target_schema[, safe, options])</p></td> |
| <td><p>Cast record batch values to another schema.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.column" title="pyarrow.RecordBatch.column"><code class="xref py py-obj docutils literal notranslate"><span class="pre">column</span></code></a>(self, i)</p></td> |
| <td><p>Select single column from Table or RecordBatch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.copy_to" title="pyarrow.RecordBatch.copy_to"><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy_to</span></code></a>(self, destination)</p></td> |
| <td><p>Copy the entire RecordBatch to destination device.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.drop_columns" title="pyarrow.RecordBatch.drop_columns"><code class="xref py py-obj docutils literal notranslate"><span class="pre">drop_columns</span></code></a>(self, columns)</p></td> |
| <td><p>Drop one or more columns and return a new Table or RecordBatch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.drop_null" title="pyarrow.RecordBatch.drop_null"><code class="xref py py-obj docutils literal notranslate"><span class="pre">drop_null</span></code></a>(self)</p></td> |
| <td><p>Remove rows that contain missing values from a Table or RecordBatch.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.equals" title="pyarrow.RecordBatch.equals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">equals</span></code></a>(self, other, bool check_metadata=False)</p></td> |
| <td><p>Check if contents of two record batches are equal.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.field" title="pyarrow.RecordBatch.field"><code class="xref py py-obj docutils literal notranslate"><span class="pre">field</span></code></a>(self, i)</p></td> |
| <td><p>Select a schema field by its column name or numeric index.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.filter" title="pyarrow.RecordBatch.filter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">filter</span></code></a>(self, mask[, null_selection_behavior])</p></td> |
| <td><p>Select rows from the table or record batch based on a boolean mask.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.from_arrays" title="pyarrow.RecordBatch.from_arrays"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_arrays</span></code></a>(list arrays[, names, schema, ...])</p></td> |
| <td><p>Construct a RecordBatch from multiple pyarrow.Arrays</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.from_pandas" title="pyarrow.RecordBatch.from_pandas"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pandas</span></code></a>(cls, df, Schema schema=None[, ...])</p></td> |
| <td><p>Convert pandas.DataFrame to an Arrow RecordBatch</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.from_pydict" title="pyarrow.RecordBatch.from_pydict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pydict</span></code></a>(cls, mapping[, schema, metadata])</p></td> |
| <td><p>Construct a Table or RecordBatch from Arrow arrays or columns.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.from_pylist" title="pyarrow.RecordBatch.from_pylist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pylist</span></code></a>(cls, mapping[, schema, metadata])</p></td> |
| <td><p>Construct a Table or RecordBatch from list of rows / dictionaries.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.from_struct_array" title="pyarrow.RecordBatch.from_struct_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_struct_array</span></code></a>(StructArray struct_array)</p></td> |
| <td><p>Construct a RecordBatch from a StructArray.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.get_total_buffer_size" title="pyarrow.RecordBatch.get_total_buffer_size"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_total_buffer_size</span></code></a>(self)</p></td> |
| <td><p>The sum of bytes in each buffer referenced by the record batch</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.itercolumns" title="pyarrow.RecordBatch.itercolumns"><code class="xref py py-obj docutils literal notranslate"><span class="pre">itercolumns</span></code></a>(self)</p></td> |
| <td><p>Iterator over all columns in their numerical order.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.remove_column" title="pyarrow.RecordBatch.remove_column"><code class="xref py py-obj docutils literal notranslate"><span class="pre">remove_column</span></code></a>(self, int i)</p></td> |
| <td><p>Create new RecordBatch with the indicated column removed.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.rename_columns" title="pyarrow.RecordBatch.rename_columns"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rename_columns</span></code></a>(self, names)</p></td> |
| <td><p>Create new record batch with columns renamed to provided names.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.replace_schema_metadata" title="pyarrow.RecordBatch.replace_schema_metadata"><code class="xref py py-obj docutils literal notranslate"><span class="pre">replace_schema_metadata</span></code></a>(self[, metadata])</p></td> |
| <td><p>Create shallow copy of record batch by replacing schema key-value metadata with the indicated new metadata (which may be None, which deletes any existing metadata</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.select" title="pyarrow.RecordBatch.select"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select</span></code></a>(self, columns)</p></td> |
| <td><p>Select columns of the RecordBatch.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.serialize" title="pyarrow.RecordBatch.serialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">serialize</span></code></a>(self[, memory_pool])</p></td> |
| <td><p>Write RecordBatch to Buffer as encapsulated IPC message, which does not include a Schema.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.set_column" title="pyarrow.RecordBatch.set_column"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_column</span></code></a>(self, int i, field_, column)</p></td> |
| <td><p>Replace column in RecordBatch at position.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.slice" title="pyarrow.RecordBatch.slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>(self[, offset, length])</p></td> |
| <td><p>Compute zero-copy slice of this RecordBatch</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.sort_by" title="pyarrow.RecordBatch.sort_by"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sort_by</span></code></a>(self, sorting, **kwargs)</p></td> |
| <td><p>Sort the Table or RecordBatch by one or multiple columns.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.take" title="pyarrow.RecordBatch.take"><code class="xref py py-obj docutils literal notranslate"><span class="pre">take</span></code></a>(self, indices)</p></td> |
| <td><p>Select rows from a Table or RecordBatch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.to_pandas" title="pyarrow.RecordBatch.to_pandas"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_pandas</span></code></a>(self[, memory_pool, categories, ...])</p></td> |
| <td><p>Convert to a pandas-compatible NumPy array or DataFrame, as appropriate</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.to_pydict" title="pyarrow.RecordBatch.to_pydict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_pydict</span></code></a>(self, *[, maps_as_pydicts])</p></td> |
| <td><p>Convert the Table or RecordBatch to a dict or OrderedDict.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.to_pylist" title="pyarrow.RecordBatch.to_pylist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_pylist</span></code></a>(self, *[, maps_as_pydicts])</p></td> |
| <td><p>Convert the Table or RecordBatch to a list of rows / dictionaries.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.to_string" title="pyarrow.RecordBatch.to_string"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_string</span></code></a>(self, *[, show_metadata, preview_cols])</p></td> |
| <td><p>Return human-readable string representation of Table or RecordBatch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.to_struct_array" title="pyarrow.RecordBatch.to_struct_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_struct_array</span></code></a>(self)</p></td> |
| <td><p>Convert to a struct array.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.to_tensor" title="pyarrow.RecordBatch.to_tensor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_tensor</span></code></a>(self, bool null_to_nan=False, ...)</p></td> |
| <td><p>Convert to a <a class="reference internal" href="pyarrow.Tensor.html#pyarrow.Tensor" title="pyarrow.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.validate" title="pyarrow.RecordBatch.validate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">validate</span></code></a>(self, *[, full])</p></td> |
| <td><p>Perform validation checks.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <p class="rubric">Attributes</p> |
| <div class="pst-scrollable-table-container"><table class="autosummary longtable table"> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.column_names" title="pyarrow.RecordBatch.column_names"><code class="xref py py-obj docutils literal notranslate"><span class="pre">column_names</span></code></a></p></td> |
| <td><p>Names of the Table or RecordBatch columns.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.columns" title="pyarrow.RecordBatch.columns"><code class="xref py py-obj docutils literal notranslate"><span class="pre">columns</span></code></a></p></td> |
| <td><p>List of all columns in numerical order.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.device_type" title="pyarrow.RecordBatch.device_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">device_type</span></code></a></p></td> |
| <td><p>The device type where the arrays in the RecordBatch reside.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.is_cpu" title="pyarrow.RecordBatch.is_cpu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cpu</span></code></a></p></td> |
| <td><p>Whether the RecordBatch's arrays are CPU-accessible.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.nbytes" title="pyarrow.RecordBatch.nbytes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nbytes</span></code></a></p></td> |
| <td><p>Total number of bytes consumed by the elements of the record batch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.num_columns" title="pyarrow.RecordBatch.num_columns"><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_columns</span></code></a></p></td> |
| <td><p>Number of columns</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.num_rows" title="pyarrow.RecordBatch.num_rows"><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_rows</span></code></a></p></td> |
| <td><p>Number of rows</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.schema" title="pyarrow.RecordBatch.schema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">schema</span></code></a></p></td> |
| <td><p>Schema of the RecordBatch and its columns</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyarrow.RecordBatch.shape" title="pyarrow.RecordBatch.shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">shape</span></code></a></p></td> |
| <td><p>Dimensions of the table or record batch: (#rows, #columns).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.__dataframe__"> |
| <span class="sig-name descname"><span class="pre">__dataframe__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nan_as_null</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.__dataframe__" title="Link to this definition">#</a></dt> |
| <dd><p>Return the dataframe interchange object implementing the interchange protocol.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>nan_as_null</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>Whether to tell the DataFrame to overwrite null values in the data |
| with <code class="docutils literal notranslate"><span class="pre">NaN</span></code> (or <code class="docutils literal notranslate"><span class="pre">NaT</span></code>).</p> |
| </dd> |
| <dt><strong>allow_copy</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#True" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">True</span></code></a></span></dt><dd><p>Whether to allow memory copying when exporting. If set to False |
| it would cause non-zero-copy exports to fail.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataFrame</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">interchange</span></code> object</dt><dd><p>The object which consuming library can use to ingress the dataframe.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Notes</p> |
| <p>Details on the interchange protocol: |
| <a class="reference external" href="https://data-apis.org/dataframe-protocol/latest/index.html">https://data-apis.org/dataframe-protocol/latest/index.html</a> |
| <cite>nan_as_null</cite> currently has no effect; once support for nullable extension |
| dtypes is added, this value should be propagated to columns.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.add_column"> |
| <span class="sig-name descname"><span class="pre">add_column</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">int</span> <span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">field_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.add_column" title="Link to this definition">#</a></dt> |
| <dd><p>Add column to RecordBatch at position i.</p> |
| <p>A new record batch is returned with the column added, the original record batch |
| object is left unchanged.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>i</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Index to place the column at.</p> |
| </dd> |
| <dt><strong>field_</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> or <a class="reference internal" href="pyarrow.Field.html#pyarrow.Field" title="pyarrow.Field"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Field</span></code></a></span></dt><dd><p>If a string is passed then the type is deduced from the column |
| data.</p> |
| </dd> |
| <dt><strong>column</strong><span class="classifier"><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> or <code class="xref py py-obj docutils literal notranslate"><span class="pre">value</span></code> coercible to <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>Column data.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd><p>New record batch with the passed column added.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Add column:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">year</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2021</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">add_column</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="s2">"year"</span><span class="p">,</span> <span class="n">year</span><span class="p">)</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [2021,2022,2019,2021]</span> |
| <span class="go">n_legs: [2,4,5,100]</span> |
| <span class="go">animals: ["Flamingo","Horse","Brittle stars","Centipede"]</span> |
| </pre></div> |
| </div> |
| <p>Original record batch is left unchanged:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">batch</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [2,4,5,100]</span> |
| <span class="go">animals: ["Flamingo","Horse","Brittle stars","Centipede"]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.append_column"> |
| <span class="sig-name descname"><span class="pre">append_column</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">field_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.append_column" title="Link to this definition">#</a></dt> |
| <dd><p>Append column at end of columns.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>field_</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> or <a class="reference internal" href="pyarrow.Field.html#pyarrow.Field" title="pyarrow.Field"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Field</span></code></a></span></dt><dd><p>If a string is passed then the type is deduced from the column |
| data.</p> |
| </dd> |
| <dt><strong>column</strong><span class="classifier"><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> or <code class="xref py py-obj docutils literal notranslate"><span class="pre">value</span></code> coercible to <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>Column data.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="pyarrow.Table.html#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a> or <a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd><p>New table or record batch with the passed column added.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Append column at the end:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">year</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2021</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">append_column</span><span class="p">(</span><span class="s1">'year'</span><span class="p">,</span> <span class="p">[</span><span class="n">year</span><span class="p">])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">year: int64</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [[2,4,5,100]]</span> |
| <span class="go">animals: [["Flamingo","Horse","Brittle stars","Centipede"]]</span> |
| <span class="go">year: [[2021,2022,2019,2021]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.cast"> |
| <span class="sig-name descname"><span class="pre">cast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Schema</span> <span class="pre">target_schema</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">safe=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options=None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.cast" title="Link to this definition">#</a></dt> |
| <dd><p>Cast record batch values to another schema.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>target_schema</strong><span class="classifier"><a class="reference internal" href="pyarrow.Schema.html#pyarrow.Schema" title="pyarrow.Schema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Schema</span></code></a></span></dt><dd><p>Schema to cast to, the names and order of fields must match.</p> |
| </dd> |
| <dt><strong>safe</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#True" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">True</span></code></a></span></dt><dd><p>Check for overflows or other unsafe conversions.</p> |
| </dd> |
| <dt><strong>options</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CastOptions</span></code>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Additional checks pass by CastOptions</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ...</span> |
| </pre></div> |
| </div> |
| <p>Define new schema and cast batch values:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_schema</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">schema</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'n_legs'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">duration</span><span class="p">(</span><span class="s1">'s'</span><span class="p">)),</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'animals'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">string</span><span class="p">())]</span> |
| <span class="gp">... </span> <span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">target_schema</span><span class="o">=</span><span class="n">my_schema</span><span class="p">)</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">n_legs: duration[s]</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [2,4,5,100]</span> |
| <span class="go">animals: ["Flamingo","Horse","Brittle stars","Centipede"]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.column"> |
| <span class="sig-name descname"><span class="pre">column</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.column" title="Link to this definition">#</a></dt> |
| <dd><p>Select single column from Table or RecordBatch.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>i</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a> or <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>The index or name of the column to retrieve.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>column</strong><span class="classifier"><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a>) or <a class="reference internal" href="pyarrow.ChunkedArray.html#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <a class="reference internal" href="pyarrow.Table.html#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a>)</span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Select a column by numeric index:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">column</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> 2,</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> 100</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| <p>Select a column by its name:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">column</span><span class="p">(</span><span class="s2">"animals"</span><span class="p">)</span> |
| <span class="go"><pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> "Flamingo",</span> |
| <span class="go"> "Horse",</span> |
| <span class="go"> "Brittle stars",</span> |
| <span class="go"> "Centipede"</span> |
| <span class="go"> ]</span> |
| <span class="go">]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.column_names"> |
| <span class="sig-name descname"><span class="pre">column_names</span></span><a class="headerlink" href="#pyarrow.RecordBatch.column_names" title="Link to this definition">#</a></dt> |
| <dd><p>Names of the Table or RecordBatch columns.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]],</span> |
| <span class="gp">... </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">'n_legs'</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">column_names</span> |
| <span class="go">['n_legs', 'animals']</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.columns"> |
| <span class="sig-name descname"><span class="pre">columns</span></span><a class="headerlink" href="#pyarrow.RecordBatch.columns" title="Link to this definition">#</a></dt> |
| <dd><p>List of all columns in numerical order.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Returns<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>columns</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> of <a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a>) or <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> of <a class="reference internal" href="pyarrow.ChunkedArray.html#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <a class="reference internal" href="pyarrow.Table.html#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a>)</span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">columns</span> |
| <span class="go">[<pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> null,</span> |
| <span class="go"> 4,</span> |
| <span class="go"> 5,</span> |
| <span class="go"> null</span> |
| <span class="go"> ]</span> |
| <span class="go">], <pyarrow.lib.ChunkedArray object at ...></span> |
| <span class="go">[</span> |
| <span class="go"> [</span> |
| <span class="go"> "Flamingo",</span> |
| <span class="go"> "Horse",</span> |
| <span class="go"> null,</span> |
| <span class="go"> "Centipede"</span> |
| <span class="go"> ]</span> |
| <span class="go">]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.copy_to"> |
| <span class="sig-name descname"><span class="pre">copy_to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.copy_to" title="Link to this definition">#</a></dt> |
| <dd><p>Copy the entire RecordBatch to destination device.</p> |
| <p>This copies each column of the record batch to create |
| a new record batch where all underlying buffers for the columns have |
| been copied to the destination MemoryManager.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>destination</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.MemoryManager</span></code> or <code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.Device</span></code></span></dt><dd><p>The destination device to copy the array to.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.device_type"> |
| <span class="sig-name descname"><span class="pre">device_type</span></span><a class="headerlink" href="#pyarrow.RecordBatch.device_type" title="Link to this definition">#</a></dt> |
| <dd><p>The device type where the arrays in the RecordBatch reside.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">DeviceAllocationType</span></code></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.drop_columns"> |
| <span class="sig-name descname"><span class="pre">drop_columns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">columns</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.drop_columns" title="Link to this definition">#</a></dt> |
| <dd><p>Drop one or more columns and return a new Table or RecordBatch.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>columns</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> or <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>]</span></dt><dd><p>Field name(s) referencing existing column(s).</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="pyarrow.Table.html#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a> or <a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd><p>A tabular object without the column(s).</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-odd">Raises<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#KeyError" title="(in Python v3.13)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KeyError</span></code></a></dt><dd><p>If any of the passed column names do not exist.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Drop one column:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">drop_columns</span><span class="p">(</span><span class="s2">"animals"</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [[2,4,5,100]]</span> |
| </pre></div> |
| </div> |
| <p>Drop one or more columns:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">drop_columns</span><span class="p">([</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">...</span> |
| <span class="go">----</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.drop_null"> |
| <span class="sig-name descname"><span class="pre">drop_null</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.drop_null" title="Link to this definition">#</a></dt> |
| <dd><p>Remove rows that contain missing values from a Table or RecordBatch.</p> |
| <p>See <a class="reference internal" href="pyarrow.compute.drop_null.html#pyarrow.compute.drop_null" title="pyarrow.compute.drop_null"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.compute.drop_null()</span></code></a> for full usage.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><a class="reference internal" href="pyarrow.Table.html#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a> or <a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd><p>A tabular object with the same schema, with rows containing |
| no missing values.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">drop_null</span><span class="p">()</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: double</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2022,2021]]</span> |
| <span class="go">n_legs: [[4,100]]</span> |
| <span class="go">animals: [["Horse","Centipede"]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.equals"> |
| <span class="sig-name descname"><span class="pre">equals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">check_metadata=False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.equals" title="Link to this definition">#</a></dt> |
| <dd><p>Check if contents of two record batches are equal.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>other</strong><span class="classifier"><a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.RecordBatch</span></code></a></span></dt><dd><p>RecordBatch to compare against.</p> |
| </dd> |
| <dt><strong>check_metadata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>Whether schema metadata equality should be checked as well.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>are_equal</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch_0</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">record_batch</span><span class="p">([])</span> |
| <span class="gp">>>> </span><span class="n">batch_1</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">})</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="go">True</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">batch_0</span><span class="p">)</span> |
| <span class="go">False</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">batch_1</span><span class="p">)</span> |
| <span class="go">True</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">batch_1</span><span class="p">,</span> <span class="n">check_metadata</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="go">False</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.field"> |
| <span class="sig-name descname"><span class="pre">field</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.field" title="Link to this definition">#</a></dt> |
| <dd><p>Select a schema field by its column name or numeric index.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>i</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a> or <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>The index or name of the field to retrieve.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="pyarrow.Field.html#pyarrow.Field" title="pyarrow.Field"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Field</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go">pyarrow.Field<n_legs: int64></span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go">pyarrow.Field<animals: string></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.filter"> |
| <span class="sig-name descname"><span class="pre">filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">null_selection_behavior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'drop'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.filter" title="Link to this definition">#</a></dt> |
| <dd><p>Select rows from the table or record batch based on a boolean mask.</p> |
| <p>The Table can be filtered based on a mask, which will be passed to |
| <a class="reference internal" href="pyarrow.compute.filter.html#pyarrow.compute.filter" title="pyarrow.compute.filter"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.compute.filter()</span></code></a> to perform the filtering, or it can |
| be filtered through a boolean <a class="reference internal" href="pyarrow.dataset.Expression.html#pyarrow.dataset.Expression" title="pyarrow.dataset.Expression"><code class="xref py py-class docutils literal notranslate"><span class="pre">Expression</span></code></a></p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>mask</strong><span class="classifier"><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> or <a class="reference internal" href="pyarrow.array.html#pyarrow.array" title="pyarrow.array"><code class="xref py py-func docutils literal notranslate"><span class="pre">array-like</span></code></a> or <a class="reference internal" href="pyarrow.dataset.Expression.html#pyarrow.dataset.Expression" title="pyarrow.dataset.Expression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Expression</span></code></a></span></dt><dd><p>The boolean mask or the <a class="reference internal" href="pyarrow.dataset.Expression.html#pyarrow.dataset.Expression" title="pyarrow.dataset.Expression"><code class="xref py py-class docutils literal notranslate"><span class="pre">Expression</span></code></a> to filter the table with.</p> |
| </dd> |
| <dt><strong>null_selection_behavior</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, default “drop”</span></dt><dd><p>How nulls in the mask should be handled, does nothing if |
| an <a class="reference internal" href="pyarrow.dataset.Expression.html#pyarrow.dataset.Expression" title="pyarrow.dataset.Expression"><code class="xref py py-class docutils literal notranslate"><span class="pre">Expression</span></code></a> is used.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>filtered</strong><span class="classifier"><a class="reference internal" href="pyarrow.Table.html#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a> or <a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></span></dt><dd><p>A tabular object of the same schema, with only the rows selected |
| by applied filtering</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Using a Table (works similarly for RecordBatch):</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">table</span><span class="p">({</span><span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| </pre></div> |
| </div> |
| <p>Define an expression and select rows:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow.compute</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pc</span> |
| <span class="gp">>>> </span><span class="n">expr</span> <span class="o">=</span> <span class="n">pc</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s2">"year"</span><span class="p">)</span> <span class="o"><=</span> <span class="mi">2020</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">expr</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2020,2019]]</span> |
| <span class="go">n_legs: [[2,5]]</span> |
| <span class="go">animals: [["Flamingo","Brittle stars"]]</span> |
| </pre></div> |
| </div> |
| <p>Define a mask and select rows:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mask</span><span class="o">=</span><span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">None</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2020,2022]]</span> |
| <span class="go">n_legs: [[2,4]]</span> |
| <span class="go">animals: [["Flamingo","Horse"]]</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">null_selection_behavior</span><span class="o">=</span><span class="s1">'emit_null'</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2020,2022,null]]</span> |
| <span class="go">n_legs: [[2,4,null]]</span> |
| <span class="go">animals: [["Flamingo","Horse",null]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.from_arrays"> |
| <em class="property"><span class="k"><span class="pre">static</span></span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_arrays</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">list</span> <span class="pre">arrays</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata=None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.from_arrays" title="Link to this definition">#</a></dt> |
| <dd><p>Construct a RecordBatch from multiple pyarrow.Arrays</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>arrays</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> of <a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.Array</span></code></a></span></dt><dd><p>One for each field in RecordBatch</p> |
| </dd> |
| <dt><strong>names</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, optional</span></dt><dd><p>Names for the batch fields. If not passed, schema must be passed</p> |
| </dd> |
| <dt><strong>schema</strong><span class="classifier"><a class="reference internal" href="pyarrow.Schema.html#pyarrow.Schema" title="pyarrow.Schema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Schema</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Schema for the created batch. If not passed, names must be passed</p> |
| </dd> |
| <dt><strong>metadata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a> or Mapping, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Optional metadata for the schema (if inferred).</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.RecordBatch</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">]</span> |
| </pre></div> |
| </div> |
| <p>Construct a RecordBatch from pyarrow Arrays using names:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="n">names</span><span class="p">)</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [2,2,4,4,5,100]</span> |
| <span class="go">animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="n">names</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animals</span> |
| <span class="go">0 2 Flamingo</span> |
| <span class="go">1 2 Parrot</span> |
| <span class="go">2 4 Dog</span> |
| <span class="go">3 4 Horse</span> |
| <span class="go">4 5 Brittle stars</span> |
| <span class="go">5 100 Centipede</span> |
| </pre></div> |
| </div> |
| <p>Construct a RecordBatch from pyarrow Arrays using schema:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_schema</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">schema</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'n_legs'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">int64</span><span class="p">()),</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'animals'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">string</span><span class="p">())],</span> |
| <span class="gp">... </span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">})</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> <span class="n">schema</span><span class="o">=</span><span class="n">my_schema</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animals</span> |
| <span class="go">0 2 Flamingo</span> |
| <span class="go">1 2 Parrot</span> |
| <span class="go">2 4 Dog</span> |
| <span class="go">3 4 Horse</span> |
| <span class="go">4 5 Brittle stars</span> |
| <span class="go">5 100 Centipede</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> <span class="n">schema</span><span class="o">=</span><span class="n">my_schema</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">n_legs: 'Number of legs per animal'</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.from_pandas"> |
| <em class="property"><span class="k"><span class="pre">classmethod</span></span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_pandas</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Schema</span> <span class="pre">schema=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserve_index=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nthreads=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">columns=None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.from_pandas" title="Link to this definition">#</a></dt> |
| <dd><p>Convert pandas.DataFrame to an Arrow RecordBatch</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>df</strong><span class="classifier"><a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.3.2)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></span></dt><dd></dd> |
| <dt><strong>schema</strong><span class="classifier"><a class="reference internal" href="pyarrow.Schema.html#pyarrow.Schema" title="pyarrow.Schema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.Schema</span></code></a>, optional</span></dt><dd><p>The expected schema of the RecordBatch. This can be used to |
| indicate the type of columns if we cannot infer it automatically. |
| If passed, the output will have exactly this schema. Columns |
| specified in the schema that are not found in the DataFrame columns |
| or its index will raise an error. Additional columns or index |
| levels in the DataFrame which are not specified in the schema will |
| be ignored.</p> |
| </dd> |
| <dt><strong>preserve_index</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, optional</span></dt><dd><p>Whether to store the index as an additional column in the resulting |
| <code class="docutils literal notranslate"><span class="pre">RecordBatch</span></code>. The default of None will store the index as a |
| column, except for RangeIndex which is stored as metadata only. Use |
| <code class="docutils literal notranslate"><span class="pre">preserve_index=True</span></code> to force it to be stored as a column.</p> |
| </dd> |
| <dt><strong>nthreads</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>If greater than 1, convert columns to Arrow in parallel using |
| indicated number of threads. By default, this follows |
| <a class="reference internal" href="pyarrow.cpu_count.html#pyarrow.cpu_count" title="pyarrow.cpu_count"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.cpu_count()</span></code></a> (may use up to system CPU count threads).</p> |
| </dd> |
| <dt><strong>columns</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>, optional</span></dt><dd><p>List of column to be converted. If None, use all columns.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.RecordBatch</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2021</span><span class="p">,</span> <span class="mi">2022</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'month'</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'day'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">13</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| </pre></div> |
| </div> |
| <p>Convert pandas DataFrame to RecordBatch:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">year: int64</span> |
| <span class="go">month: int64</span> |
| <span class="go">day: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [2020,2022,2021,2022]</span> |
| <span class="go">month: [3,5,7,9]</span> |
| <span class="go">day: [1,5,9,13]</span> |
| <span class="go">n_legs: [2,4,5,100]</span> |
| <span class="go">animals: ["Flamingo","Horse","Brittle stars","Centipede"]</span> |
| </pre></div> |
| </div> |
| <p>Convert pandas DataFrame to RecordBatch using schema:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_schema</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">schema</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'n_legs'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">int64</span><span class="p">()),</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'animals'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">string</span><span class="p">())],</span> |
| <span class="gp">... </span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">})</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">schema</span><span class="o">=</span><span class="n">my_schema</span><span class="p">)</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [2,4,5,100]</span> |
| <span class="go">animals: ["Flamingo","Horse","Brittle stars","Centipede"]</span> |
| </pre></div> |
| </div> |
| <p>Convert pandas DataFrame to RecordBatch specifying columns:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">])</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [2,4,5,100]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.from_pydict"> |
| <em class="property"><span class="k"><span class="pre">classmethod</span></span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_pydict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.from_pydict" title="Link to this definition">#</a></dt> |
| <dd><p>Construct a Table or RecordBatch from Arrow arrays or columns.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>mapping</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a> or Mapping</span></dt><dd><p>A mapping of strings to Arrays or Python lists.</p> |
| </dd> |
| <dt><strong>schema</strong><span class="classifier"><a class="reference internal" href="pyarrow.Schema.html#pyarrow.Schema" title="pyarrow.Schema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Schema</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>If not passed, will be inferred from the Mapping values.</p> |
| </dd> |
| <dt><strong>metadata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a> or Mapping, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Optional metadata for the schema (if inferred).</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="pyarrow.Table.html#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a> or <a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">pydict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="n">n_legs</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">:</span> <span class="n">animals</span><span class="p">}</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a dictionary of arrays:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pydict</span><span class="p">(</span><span class="n">pydict</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [[2,4,5,100]]</span> |
| <span class="go">animals: [["Flamingo","Horse","Brittle stars","Centipede"]]</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pydict</span><span class="p">(</span><span class="n">pydict</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a dictionary of arrays with metadata:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">}</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pydict</span><span class="p">(</span><span class="n">pydict</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="n">my_metadata</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">n_legs: 'Number of legs per animal'</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a dictionary of arrays with pyarrow schema:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_schema</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">schema</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'n_legs'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">int64</span><span class="p">()),</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'animals'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">string</span><span class="p">())],</span> |
| <span class="gp">... </span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">})</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pydict</span><span class="p">(</span><span class="n">pydict</span><span class="p">,</span> <span class="n">schema</span><span class="o">=</span><span class="n">my_schema</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">n_legs: 'Number of legs per animal'</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.from_pylist"> |
| <em class="property"><span class="k"><span class="pre">classmethod</span></span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_pylist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.from_pylist" title="Link to this definition">#</a></dt> |
| <dd><p>Construct a Table or RecordBatch from list of rows / dictionaries.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>mapping</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> of dicts of rows</span></dt><dd><p>A mapping of strings to row values.</p> |
| </dd> |
| <dt><strong>schema</strong><span class="classifier"><a class="reference internal" href="pyarrow.Schema.html#pyarrow.Schema" title="pyarrow.Schema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Schema</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>If not passed, will be inferred from the first row of the |
| mapping values.</p> |
| </dd> |
| <dt><strong>metadata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a> or Mapping, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Optional metadata for the schema (if inferred).</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="pyarrow.Table.html#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a> or <a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">pylist</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">:</span> <span class="s1">'Flamingo'</span><span class="p">},</span> |
| <span class="gp">... </span> <span class="p">{</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">:</span> <span class="s1">'Dog'</span><span class="p">}]</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a list of rows:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pylist</span><span class="p">(</span><span class="n">pylist</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [[2,4]]</span> |
| <span class="go">animals: [["Flamingo","Dog"]]</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a list of rows with metadata:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">}</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pylist</span><span class="p">(</span><span class="n">pylist</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="n">my_metadata</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">n_legs: 'Number of legs per animal'</span> |
| </pre></div> |
| </div> |
| <p>Construct a Table from a list of rows with pyarrow schema:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_schema</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">schema</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'n_legs'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">int64</span><span class="p">()),</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'animals'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">string</span><span class="p">())],</span> |
| <span class="gp">... </span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">})</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pylist</span><span class="p">(</span><span class="n">pylist</span><span class="p">,</span> <span class="n">schema</span><span class="o">=</span><span class="n">my_schema</span><span class="p">)</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">n_legs: 'Number of legs per animal'</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.from_struct_array"> |
| <em class="property"><span class="k"><span class="pre">static</span></span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_struct_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">StructArray</span> <span class="pre">struct_array</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.from_struct_array" title="Link to this definition">#</a></dt> |
| <dd><p>Construct a RecordBatch from a StructArray.</p> |
| <p>Each field in the StructArray will become a column in the resulting |
| <code class="docutils literal notranslate"><span class="pre">RecordBatch</span></code>.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>struct_array</strong><span class="classifier"><a class="reference internal" href="pyarrow.StructArray.html#pyarrow.StructArray" title="pyarrow.StructArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StructArray</span></code></a></span></dt><dd><p>Array to construct the record batch from.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.RecordBatch</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">struct</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([{</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">:</span> <span class="s1">'Parrot'</span><span class="p">},</span> |
| <span class="gp">... </span> <span class="p">{</span><span class="s1">'year'</span><span class="p">:</span> <span class="mi">2022</span><span class="p">,</span> <span class="s1">'n_legs'</span><span class="p">:</span> <span class="mi">4</span><span class="p">}])</span> |
| <span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_struct_array</span><span class="p">(</span><span class="n">struct</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> animals n_legs year</span> |
| <span class="go">0 Parrot 2 NaN</span> |
| <span class="go">1 None 4 2022.0</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.get_total_buffer_size"> |
| <span class="sig-name descname"><span class="pre">get_total_buffer_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.get_total_buffer_size" title="Link to this definition">#</a></dt> |
| <dd><p>The sum of bytes in each buffer referenced by the record batch</p> |
| <p>An array may only reference a portion of a buffer. |
| This method will overestimate in this case and return the |
| byte size of the entire buffer.</p> |
| <p>If a buffer is referenced multiple times then it will |
| only be counted once.</p> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">get_total_buffer_size</span><span class="p">()</span> |
| <span class="go">120</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.is_cpu"> |
| <span class="sig-name descname"><span class="pre">is_cpu</span></span><a class="headerlink" href="#pyarrow.RecordBatch.is_cpu" title="Link to this definition">#</a></dt> |
| <dd><p>Whether the RecordBatch’s arrays are CPU-accessible.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.itercolumns"> |
| <span class="sig-name descname"><span class="pre">itercolumns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.itercolumns" title="Link to this definition">#</a></dt> |
| <dd><p>Iterator over all columns in their numerical order.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Yields<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a>) or <a class="reference internal" href="pyarrow.ChunkedArray.html#pyarrow.ChunkedArray" title="pyarrow.ChunkedArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChunkedArray</span></code></a> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <a class="reference internal" href="pyarrow.Table.html#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a>)</dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">table</span><span class="o">.</span><span class="n">itercolumns</span><span class="p">():</span> |
| <span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">null_count</span><span class="p">)</span> |
| <span class="gp">...</span> |
| <span class="go">2</span> |
| <span class="go">1</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.nbytes"> |
| <span class="sig-name descname"><span class="pre">nbytes</span></span><a class="headerlink" href="#pyarrow.RecordBatch.nbytes" title="Link to this definition">#</a></dt> |
| <dd><p>Total number of bytes consumed by the elements of the record batch.</p> |
| <p>In other words, the sum of bytes from all buffer ranges referenced.</p> |
| <p>Unlike <cite>get_total_buffer_size</cite> this method will account for array |
| offsets.</p> |
| <p>If buffers are shared between arrays then the shared |
| portion will only be counted multiple times.</p> |
| <p>The dictionary of dictionary arrays will always be counted in their |
| entirety even if the array only references a portion of the dictionary.</p> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">nbytes</span> |
| <span class="go">116</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.num_columns"> |
| <span class="sig-name descname"><span class="pre">num_columns</span></span><a class="headerlink" href="#pyarrow.RecordBatch.num_columns" title="Link to this definition">#</a></dt> |
| <dd><p>Number of columns</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">num_columns</span> |
| <span class="go">2</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.num_rows"> |
| <span class="sig-name descname"><span class="pre">num_rows</span></span><a class="headerlink" href="#pyarrow.RecordBatch.num_rows" title="Link to this definition">#</a></dt> |
| <dd><p>Number of rows</p> |
| <p>Due to the definition of a RecordBatch, all columns have the same |
| number of rows.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">num_rows</span> |
| <span class="go">6</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.remove_column"> |
| <span class="sig-name descname"><span class="pre">remove_column</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">int</span> <span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.remove_column" title="Link to this definition">#</a></dt> |
| <dd><p>Create new RecordBatch with the indicated column removed.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>i</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Index of column to remove.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="pyarrow.Table.html#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a></dt><dd><p>New record batch without the column.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">remove_column</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [2,4,5,100]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.rename_columns"> |
| <span class="sig-name descname"><span class="pre">rename_columns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">names</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.rename_columns" title="Link to this definition">#</a></dt> |
| <dd><p>Create new record batch with columns renamed to provided names.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>names</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>] or <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>]</span></dt><dd><p>List of new column names or mapping of old column names to new column names.</p> |
| <p>If a mapping of old to new column names is passed, then all columns which are |
| found to match a provided old column name will be renamed to the new column name. |
| If any column names are not found in the mapping, a KeyError will be raised.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| <dt class="field-odd">Raises<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#KeyError" title="(in Python v3.13)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KeyError</span></code></a></dt><dd><p>If any of the column names passed in the names mapping do not exist.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">new_names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"n"</span><span class="p">,</span> <span class="s2">"name"</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">rename_columns</span><span class="p">(</span><span class="n">new_names</span><span class="p">)</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">n: int64</span> |
| <span class="go">name: string</span> |
| <span class="go">----</span> |
| <span class="go">n: [2,4,5,100]</span> |
| <span class="go">name: ["Flamingo","Horse","Brittle stars","Centipede"]</span> |
| <span class="gp">>>> </span><span class="n">new_names</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"n"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">:</span> <span class="s2">"name"</span><span class="p">}</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">rename_columns</span><span class="p">(</span><span class="n">new_names</span><span class="p">)</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">n: int64</span> |
| <span class="go">name: string</span> |
| <span class="go">----</span> |
| <span class="go">n: [2,4,5,100]</span> |
| <span class="go">name: ["Flamingo","Horse","Brittle stars","Centipede"]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.replace_schema_metadata"> |
| <span class="sig-name descname"><span class="pre">replace_schema_metadata</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.replace_schema_metadata" title="Link to this definition">#</a></dt> |
| <dd><p>Create shallow copy of record batch by replacing schema |
| key-value metadata with the indicated new metadata (which may be None, |
| which deletes any existing metadata</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>metadata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>shallow_copy</strong><span class="classifier"><a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| <p>Constructing a RecordBatch with schema and metadata:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_schema</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">schema</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="s1">'n_legs'</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">int64</span><span class="p">())],</span> |
| <span class="gp">... </span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"n_legs"</span><span class="p">:</span> <span class="s2">"Number of legs per animal"</span><span class="p">})</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">],</span> <span class="n">schema</span><span class="o">=</span><span class="n">my_schema</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">-- schema metadata --</span> |
| <span class="go">n_legs: 'Number of legs per animal'</span> |
| </pre></div> |
| </div> |
| <p>Shallow copy of a RecordBatch with deleted schema metadata:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">replace_schema_metadata</span><span class="p">()</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.schema"> |
| <span class="sig-name descname"><span class="pre">schema</span></span><a class="headerlink" href="#pyarrow.RecordBatch.schema" title="Link to this definition">#</a></dt> |
| <dd><p>Schema of the RecordBatch and its columns</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><a class="reference internal" href="pyarrow.Schema.html#pyarrow.Schema" title="pyarrow.Schema"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.Schema</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">schema</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.select"> |
| <span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">columns</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.select" title="Link to this definition">#</a></dt> |
| <dd><p>Select columns of the RecordBatch.</p> |
| <p>Returns a new RecordBatch with the specified columns, and metadata |
| preserved.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><strong>columns</strong><span class="classifier">list-like</span></dt><dd><p>The column names or integer indices to select.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">record_batch</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| <p>Select columns my indices:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">select</span><span class="p">([</span><span class="mi">1</span><span class="p">])</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]</span> |
| </pre></div> |
| </div> |
| <p>Select columns by names:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">select</span><span class="p">([</span><span class="s2">"n_legs"</span><span class="p">])</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [2,2,4,4,5,100]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.serialize"> |
| <span class="sig-name descname"><span class="pre">serialize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_pool</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.serialize" title="Link to this definition">#</a></dt> |
| <dd><p>Write RecordBatch to Buffer as encapsulated IPC message, which does not |
| include a Schema.</p> |
| <p>To reconstruct a RecordBatch from the encapsulated IPC message Buffer |
| returned by this function, a Schema must be passed separately. See |
| Examples.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>memory_pool</strong><span class="classifier"><a class="reference internal" href="pyarrow.MemoryPool.html#pyarrow.MemoryPool" title="pyarrow.MemoryPool"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MemoryPool</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Uses default memory pool if not specified</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>serialized</strong><span class="classifier"><a class="reference internal" href="pyarrow.Buffer.html#pyarrow.Buffer" title="pyarrow.Buffer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Buffer</span></code></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">buf</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">serialize</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">buf</span> |
| <span class="go"><pyarrow.Buffer address=0x... size=... is_cpu=True is_mutable=True></span> |
| </pre></div> |
| </div> |
| <p>Reconstruct RecordBatch from IPC message Buffer and original Schema</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">ipc</span><span class="o">.</span><span class="n">read_record_batch</span><span class="p">(</span><span class="n">buf</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">schema</span><span class="p">)</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [2,2,4,4,5,100]</span> |
| <span class="go">animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.set_column"> |
| <span class="sig-name descname"><span class="pre">set_column</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">int</span> <span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">field_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.set_column" title="Link to this definition">#</a></dt> |
| <dd><p>Replace column in RecordBatch at position.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>i</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Index to place the column at.</p> |
| </dd> |
| <dt><strong>field_</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> or <a class="reference internal" href="pyarrow.Field.html#pyarrow.Field" title="pyarrow.Field"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Field</span></code></a></span></dt><dd><p>If a string is passed then the type is deduced from the column |
| data.</p> |
| </dd> |
| <dt><strong>column</strong><span class="classifier"><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> or <code class="xref py py-obj docutils literal notranslate"><span class="pre">value</span></code> coercible to <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>Column data.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd><p>New record batch with the passed column set.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Replace a column:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">year</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2021</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">set_column</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="s1">'year'</span><span class="p">,</span> <span class="n">year</span><span class="p">)</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">year: int64</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [2,4,5,100]</span> |
| <span class="go">year: [2021,2022,2019,2021]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py attribute"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.shape"> |
| <span class="sig-name descname"><span class="pre">shape</span></span><a class="headerlink" href="#pyarrow.RecordBatch.shape" title="Link to this definition">#</a></dt> |
| <dd><p>Dimensions of the table or record batch: (#rows, #columns).</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt>(<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>)</dt><dd><p>Number of rows and number of columns.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">table</span><span class="p">({</span><span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">shape</span> |
| <span class="go">(4, 2)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.slice"> |
| <span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.slice" title="Link to this definition">#</a></dt> |
| <dd><p>Compute zero-copy slice of this RecordBatch</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>offset</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, default 0</span></dt><dd><p>Offset from start of record batch to slice</p> |
| </dd> |
| <dt><strong>length</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Length of slice (default is until end of batch starting from |
| offset)</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl> |
| <dt><strong>sliced</strong><span class="classifier"><a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></span></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animals</span> |
| <span class="go">0 2 Flamingo</span> |
| <span class="go">1 2 Parrot</span> |
| <span class="go">2 4 Dog</span> |
| <span class="go">3 4 Horse</span> |
| <span class="go">4 5 Brittle stars</span> |
| <span class="go">5 100 Centipede</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">slice</span><span class="p">(</span><span class="n">offset</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animals</span> |
| <span class="go">0 4 Horse</span> |
| <span class="go">1 5 Brittle stars</span> |
| <span class="go">2 100 Centipede</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">slice</span><span class="p">(</span><span class="n">length</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animals</span> |
| <span class="go">0 2 Flamingo</span> |
| <span class="go">1 2 Parrot</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">slice</span><span class="p">(</span><span class="n">offset</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">length</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animals</span> |
| <span class="go">0 4 Horse</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.sort_by"> |
| <span class="sig-name descname"><span class="pre">sort_by</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sorting</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.sort_by" title="Link to this definition">#</a></dt> |
| <dd><p>Sort the Table or RecordBatch by one or multiple columns.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>sorting</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> or <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a>(<code class="xref py py-obj docutils literal notranslate"><span class="pre">name</span></code>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">order</span></code>)]</span></dt><dd><p>Name of the column to use to sort (ascending), or |
| a list of multiple sorting conditions where |
| each entry is a tuple with column name |
| and sorting order (“ascending” or “descending”)</p> |
| </dd> |
| <dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a>, optional</span></dt><dd><p>Additional sorting options. |
| As allowed by <code class="xref py py-class docutils literal notranslate"><span class="pre">SortOptions</span></code></p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="pyarrow.Table.html#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a> or <a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd><p>A new tabular object sorted according to the sort keys.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2021</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animal'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> |
| <span class="gp">... </span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">sort_by</span><span class="p">(</span><span class="s1">'animal'</span><span class="p">)</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animal: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2019,2021,2021,2020,2022,2022]]</span> |
| <span class="go">n_legs: [[5,100,4,2,4,2]]</span> |
| <span class="go">animal: [["Brittle stars","Centipede","Dog","Flamingo","Horse","Parrot"]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.take"> |
| <span class="sig-name descname"><span class="pre">take</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.take" title="Link to this definition">#</a></dt> |
| <dd><p>Select rows from a Table or RecordBatch.</p> |
| <p>See <a class="reference internal" href="pyarrow.compute.take.html#pyarrow.compute.take" title="pyarrow.compute.take"><code class="xref py py-func docutils literal notranslate"><span class="pre">pyarrow.compute.take()</span></code></a> for full usage.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>indices</strong><span class="classifier"><a class="reference internal" href="pyarrow.Array.html#pyarrow.Array" title="pyarrow.Array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Array</span></code></a> or <a class="reference internal" href="pyarrow.array.html#pyarrow.array" title="pyarrow.array"><code class="xref py py-func docutils literal notranslate"><span class="pre">array-like</span></code></a></span></dt><dd><p>The indices in the tabular object whose rows will be returned.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference internal" href="pyarrow.Table.html#pyarrow.Table" title="pyarrow.Table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Table</span></code></a> or <a class="reference internal" href="#pyarrow.RecordBatch" title="pyarrow.RecordBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordBatch</span></code></a></dt><dd><p>A tabular object with the same schema, containing the taken rows.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| <span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'year'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2021</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'n_legs'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'animals'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_pandas</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">take</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span> |
| <span class="go">pyarrow.Table</span> |
| <span class="go">year: int64</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">year: [[2022,2021]]</span> |
| <span class="go">n_legs: [[4,100]]</span> |
| <span class="go">animals: [["Horse","Centipede"]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.to_pandas"> |
| <span class="sig-name descname"><span class="pre">to_pandas</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_pool=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categories=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">strings_to_categorical=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">zero_copy_only=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">integer_object_nulls=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">date_as_object=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">timestamp_as_object=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">use_threads=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">deduplicate_objects=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">ignore_metadata=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">safe=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">split_blocks=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">self_destruct=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">str</span> <span class="pre">maps_as_pydicts=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">types_mapper=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">coerce_temporal_nanoseconds=False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.to_pandas" title="Link to this definition">#</a></dt> |
| <dd><p>Convert to a pandas-compatible NumPy array or DataFrame, as appropriate</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>memory_pool</strong><span class="classifier"><a class="reference internal" href="pyarrow.MemoryPool.html#pyarrow.MemoryPool" title="pyarrow.MemoryPool"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MemoryPool</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Arrow MemoryPool to use for allocations. Uses the default memory |
| pool if not passed.</p> |
| </dd> |
| <dt><strong>categories</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>, default <code class="xref py py-obj docutils literal notranslate"><span class="pre">empty</span></code></span></dt><dd><p>List of fields that should be returned as pandas.Categorical. Only |
| applies to table-like data structures.</p> |
| </dd> |
| <dt><strong>strings_to_categorical</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>Encode string (UTF8) and binary types to pandas.Categorical.</p> |
| </dd> |
| <dt><strong>zero_copy_only</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>Raise an ArrowException if this function call would require copying |
| the underlying data.</p> |
| </dd> |
| <dt><strong>integer_object_nulls</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>Cast integers with nulls to objects</p> |
| </dd> |
| <dt><strong>date_as_object</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#True" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">True</span></code></a></span></dt><dd><p>Cast dates to objects. If False, convert to datetime64 dtype with |
| the equivalent time unit (if supported). Note: in pandas version |
| < 2.0, only datetime64[ns] conversion is supported.</p> |
| </dd> |
| <dt><strong>timestamp_as_object</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>Cast non-nanosecond timestamps (np.datetime64) to objects. This is |
| useful in pandas version 1.x if you have timestamps that don’t fit |
| in the normal date range of nanosecond timestamps (1678 CE-2262 CE). |
| Non-nanosecond timestamps are supported in pandas version 2.0. |
| If False, all timestamps are converted to datetime64 dtype.</p> |
| </dd> |
| <dt><strong>use_threads</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#True" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">True</span></code></a></span></dt><dd><p>Whether to parallelize the conversion using multiple threads.</p> |
| </dd> |
| <dt><strong>deduplicate_objects</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#True" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">True</span></code></a></span></dt><dd><p>Do not create multiple copies Python objects when created, to save |
| on memory use. Conversion will be slower.</p> |
| </dd> |
| <dt><strong>ignore_metadata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>If True, do not use the ‘pandas’ metadata to reconstruct the |
| DataFrame index, if present</p> |
| </dd> |
| <dt><strong>safe</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#True" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">True</span></code></a></span></dt><dd><p>For certain data types, a cast is needed in order to store the |
| data in a pandas DataFrame or Series (e.g. timestamps are always |
| stored as nanoseconds in pandas). This option controls whether it |
| is a safe cast or not.</p> |
| </dd> |
| <dt><strong>split_blocks</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>If True, generate one internal “block” for each column when |
| creating a pandas.DataFrame from a RecordBatch or Table. While this |
| can temporarily reduce memory note that various pandas operations |
| can trigger “consolidation” which may balloon memory use.</p> |
| </dd> |
| <dt><strong>self_destruct</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>EXPERIMENTAL: If True, attempt to deallocate the originating Arrow |
| memory while converting the Arrow object to pandas. If you use the |
| object after calling to_pandas with this option it will crash your |
| program.</p> |
| <p>Note that you may not see always memory usage improvements. For |
| example, if multiple columns share an underlying allocation, |
| memory can’t be freed until all columns are converted.</p> |
| </dd> |
| <dt><strong>maps_as_pydicts</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, optional, default <cite>None</cite></span></dt><dd><p>Valid values are <cite>None</cite>, ‘lossy’, or ‘strict’. |
| The default behavior (<cite>None</cite>), is to convert Arrow Map arrays to |
| Python association lists (list-of-tuples) in the same order as the |
| Arrow Map, as in [(key1, value1), (key2, value2), …].</p> |
| <p>If ‘lossy’ or ‘strict’, convert Arrow Map arrays to native Python dicts. |
| This can change the ordering of (key, value) pairs, and will |
| deduplicate multiple keys, resulting in a possible loss of data.</p> |
| <p>If ‘lossy’, this key deduplication results in a warning printed |
| when detected. If ‘strict’, this instead results in an exception |
| being raised when detected.</p> |
| </dd> |
| <dt><strong>types_mapper</strong><span class="classifier">function, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>A function mapping a pyarrow DataType to a pandas ExtensionDtype. |
| This can be used to override the default pandas type for conversion |
| of built-in pyarrow types or in absence of pandas_metadata in the |
| Table schema. The function receives a pyarrow DataType and is |
| expected to return a pandas ExtensionDtype or <code class="docutils literal notranslate"><span class="pre">None</span></code> if the |
| default conversion should be used for that type. If you have |
| a dictionary mapping, you can pass <code class="docutils literal notranslate"><span class="pre">dict.get</span></code> as function.</p> |
| </dd> |
| <dt><strong>coerce_temporal_nanoseconds</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>Only applicable to pandas version >= 2.0. |
| A legacy option to coerce date32, date64, duration, and timestamp |
| time units to nanoseconds when converting to pandas. This is the |
| default behavior in pandas version 1.x. Set this option to True if |
| you’d like to use this coercion when using pandas version >= 2.0 |
| for backwards compatibility (not recommended otherwise).</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.3.2)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.Series</span></code></a> or <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.3.2)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a> depending on <a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.13)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code></a> of object</dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> |
| </pre></div> |
| </div> |
| <p>Convert a Table to pandas DataFrame:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">table</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]),</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">... </span> <span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">'n_legs'</span><span class="p">,</span> <span class="s1">'animals'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animals</span> |
| <span class="go">0 2 Flamingo</span> |
| <span class="go">1 4 Horse</span> |
| <span class="go">2 5 Brittle stars</span> |
| <span class="go">3 100 Centipede</span> |
| <span class="gp">>>> </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">table</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">(),</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> |
| <span class="go">True</span> |
| </pre></div> |
| </div> |
| <p>Convert a RecordBatch to pandas DataFrame:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">record_batch</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">batch</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">n_legs: int64</span> |
| <span class="go">animals: string</span> |
| <span class="go">----</span> |
| <span class="go">n_legs: [2,4,5,100]</span> |
| <span class="go">animals: ["Flamingo","Horse","Brittle stars","Centipede"]</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go"> n_legs animals</span> |
| <span class="go">0 2 Flamingo</span> |
| <span class="go">1 4 Horse</span> |
| <span class="go">2 5 Brittle stars</span> |
| <span class="go">3 100 Centipede</span> |
| <span class="gp">>>> </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">(),</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> |
| <span class="go">True</span> |
| </pre></div> |
| </div> |
| <p>Convert a Chunked Array to pandas Series:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">chunked_array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span> |
| <span class="go">0 2</span> |
| <span class="go">1 2</span> |
| <span class="go">2 4</span> |
| <span class="go">3 4</span> |
| <span class="go">4 5</span> |
| <span class="go">5 100</span> |
| <span class="go">dtype: int64</span> |
| <span class="gp">>>> </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">n_legs</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">(),</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)</span> |
| <span class="go">True</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.to_pydict"> |
| <span class="sig-name descname"><span class="pre">to_pydict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="keyword-only-separator o"><abbr title="Keyword-only parameters separator (PEP 3102)"><span class="pre">*</span></abbr></span></em>, <em class="sig-param"><span class="n"><span class="pre">maps_as_pydicts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.to_pydict" title="Link to this definition">#</a></dt> |
| <dd><p>Convert the Table or RecordBatch to a dict or OrderedDict.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>maps_as_pydicts</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, optional, default <cite>None</cite></span></dt><dd><p>Valid values are <cite>None</cite>, ‘lossy’, or ‘strict’. |
| The default behavior (<cite>None</cite>), is to convert Arrow Map arrays to |
| Python association lists (list-of-tuples) in the same order as the |
| Arrow Map, as in [(key1, value1), (key2, value2), …].</p> |
| <p>If ‘lossy’ or ‘strict’, convert Arrow Map arrays to native Python dicts.</p> |
| <p>If ‘lossy’, whenever duplicate keys are detected, a warning will be printed. |
| The last seen value of a duplicate key will be in the Python dictionary. |
| If ‘strict’, this instead results in an exception being raised when detected.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">n_legs</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">animals</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Parrot"</span><span class="p">,</span> <span class="s2">"Dog"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">([</span><span class="n">n_legs</span><span class="p">,</span> <span class="n">animals</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">to_pydict</span><span class="p">()</span> |
| <span class="go">{'n_legs': [2, 2, 4, 4, 5, 100], 'animals': ['Flamingo', 'Parrot', ..., 'Centipede']}</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.to_pylist"> |
| <span class="sig-name descname"><span class="pre">to_pylist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="keyword-only-separator o"><abbr title="Keyword-only parameters separator (PEP 3102)"><span class="pre">*</span></abbr></span></em>, <em class="sig-param"><span class="n"><span class="pre">maps_as_pydicts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.to_pylist" title="Link to this definition">#</a></dt> |
| <dd><p>Convert the Table or RecordBatch to a list of rows / dictionaries.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>maps_as_pydicts</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, optional, default <cite>None</cite></span></dt><dd><p>Valid values are <cite>None</cite>, ‘lossy’, or ‘strict’. |
| The default behavior (<cite>None</cite>), is to convert Arrow Map arrays to |
| Python association lists (list-of-tuples) in the same order as the |
| Arrow Map, as in [(key1, value1), (key2, value2), …].</p> |
| <p>If ‘lossy’ or ‘strict’, convert Arrow Map arrays to native Python dicts.</p> |
| <p>If ‘lossy’, whenever duplicate keys are detected, a warning will be printed. |
| The last seen value of a duplicate key will be in the Python dictionary. |
| If ‘strict’, this instead results in an exception being raised when detected.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>Table (works similarly for RecordBatch)</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="p">[</span><span class="s2">"Flamingo"</span><span class="p">,</span> <span class="s2">"Horse"</span><span class="p">,</span> <span class="s2">"Brittle stars"</span><span class="p">,</span> <span class="s2">"Centipede"</span><span class="p">]]</span> |
| <span class="gp">>>> </span><span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">"n_legs"</span><span class="p">,</span> <span class="s2">"animals"</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">table</span><span class="o">.</span><span class="n">to_pylist</span><span class="p">()</span> |
| <span class="go">[{'n_legs': 2, 'animals': 'Flamingo'}, {'n_legs': 4, 'animals': 'Horse'}, ...</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.to_string"> |
| <span class="sig-name descname"><span class="pre">to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="keyword-only-separator o"><abbr title="Keyword-only parameters separator (PEP 3102)"><span class="pre">*</span></abbr></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preview_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.to_string" title="Link to this definition">#</a></dt> |
| <dd><p>Return human-readable string representation of Table or RecordBatch.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>show_metadata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>Display Field-level and Schema-level KeyValueMetadata.</p> |
| </dd> |
| <dt><strong>preview_cols</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, default 0</span></dt><dd><p>Display values of the columns for the first N columns.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.to_struct_array"> |
| <span class="sig-name descname"><span class="pre">to_struct_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.to_struct_array" title="Link to this definition">#</a></dt> |
| <dd><p>Convert to a struct array.</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.to_tensor"> |
| <span class="sig-name descname"><span class="pre">to_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">null_to_nan=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool</span> <span class="pre">row_major=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">MemoryPool</span> <span class="pre">memory_pool=None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.to_tensor" title="Link to this definition">#</a></dt> |
| <dd><p>Convert to a <a class="reference internal" href="pyarrow.Tensor.html#pyarrow.Tensor" title="pyarrow.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a>.</p> |
| <p>RecordBatches that can be converted have fields of type signed or unsigned |
| integer or float, including all bit-widths.</p> |
| <p><code class="docutils literal notranslate"><span class="pre">null_to_nan</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code> by default and this method will raise an error in case |
| any nulls are present. RecordBatches with nulls can be converted with <code class="docutils literal notranslate"><span class="pre">null_to_nan</span></code> |
| set to <code class="docutils literal notranslate"><span class="pre">True</span></code>. In this case null values are converted to <code class="docutils literal notranslate"><span class="pre">NaN</span></code> and integer type |
| arrays are promoted to the appropriate float type.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>null_to_nan</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>Whether to write null values in the result as <code class="docutils literal notranslate"><span class="pre">NaN</span></code>.</p> |
| </dd> |
| <dt><strong>row_major</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#True" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">True</span></code></a></span></dt><dd><p>Whether resulting Tensor is row-major or column-major</p> |
| </dd> |
| <dt><strong>memory_pool</strong><span class="classifier"><a class="reference internal" href="pyarrow.MemoryPool.html#pyarrow.MemoryPool" title="pyarrow.MemoryPool"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MemoryPool</span></code></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>For memory allocations, if required, otherwise use default pool</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span> |
| <span class="gp">>>> </span><span class="n">batch</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">record_batch</span><span class="p">(</span> |
| <span class="gp">... </span> <span class="p">[</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="nb">type</span><span class="o">=</span><span class="n">pa</span><span class="o">.</span><span class="n">int32</span><span class="p">()),</span> |
| <span class="gp">... </span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="nb">type</span><span class="o">=</span><span class="n">pa</span><span class="o">.</span><span class="n">float32</span><span class="p">()),</span> |
| <span class="gp">... </span> <span class="p">],</span> <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"a"</span><span class="p">,</span> <span class="s2">"b"</span><span class="p">]</span> |
| <span class="gp">... </span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">batch</span> |
| <span class="go">pyarrow.RecordBatch</span> |
| <span class="go">a: int32</span> |
| <span class="go">b: float</span> |
| <span class="go">----</span> |
| <span class="go">a: [1,2,3,4,null]</span> |
| <span class="go">b: [10,20,30,40,null]</span> |
| </pre></div> |
| </div> |
| <p>Convert a RecordBatch to row-major Tensor with null values |
| written as <a href="#id1"><span class="problematic" id="id2">``</span></a>NaN``s</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">to_tensor</span><span class="p">(</span><span class="n">null_to_nan</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="go"><pyarrow.Tensor></span> |
| <span class="go">type: double</span> |
| <span class="go">shape: (5, 2)</span> |
| <span class="go">strides: (16, 8)</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">to_tensor</span><span class="p">(</span><span class="n">null_to_nan</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span> |
| <span class="go">array([[ 1., 10.],</span> |
| <span class="go"> [ 2., 20.],</span> |
| <span class="go"> [ 3., 30.],</span> |
| <span class="go"> [ 4., 40.],</span> |
| <span class="go"> [nan, nan]])</span> |
| </pre></div> |
| </div> |
| <p>Convert a RecordBatch to column-major Tensor</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">to_tensor</span><span class="p">(</span><span class="n">null_to_nan</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">row_major</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="go"><pyarrow.Tensor></span> |
| <span class="go">type: double</span> |
| <span class="go">shape: (5, 2)</span> |
| <span class="go">strides: (8, 40)</span> |
| <span class="gp">>>> </span><span class="n">batch</span><span class="o">.</span><span class="n">to_tensor</span><span class="p">(</span><span class="n">null_to_nan</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">row_major</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span> |
| <span class="go">array([[ 1., 10.],</span> |
| <span class="go"> [ 2., 20.],</span> |
| <span class="go"> [ 3., 30.],</span> |
| <span class="go"> [ 4., 40.],</span> |
| <span class="go"> [nan, nan]])</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt class="sig sig-object py" id="pyarrow.RecordBatch.validate"> |
| <span class="sig-name descname"><span class="pre">validate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="keyword-only-separator o"><abbr title="Keyword-only parameters separator (PEP 3102)"><span class="pre">*</span></abbr></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.RecordBatch.validate" title="Link to this definition">#</a></dt> |
| <dd><p>Perform validation checks. An exception is raised if validation fails.</p> |
| <p>By default only cheap validation checks are run. Pass <cite>full=True</cite> |
| for thorough validation checks (potentially O(n)).</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters<span class="colon">:</span></dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>full</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.13)"><span class="xref std std-ref">bool</span></a>, default <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.13)"><code class="docutils literal notranslate"><span class="pre">False</span></code></a></span></dt><dd><p>If True, run expensive checks, otherwise cheap checks only.</p> |
| </dd> |
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| <dt class="field-even">Raises<span class="colon">:</span></dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">ArrowInvalid</span></code></dt><dd></dd> |
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| <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch"><code class="docutils literal notranslate"><span class="pre">RecordBatch</span></code></a><ul class="visible nav section-nav flex-column"> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.__init__"><code class="docutils literal notranslate"><span class="pre">RecordBatch.__init__()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.__dataframe__"><code class="docutils literal notranslate"><span class="pre">RecordBatch.__dataframe__()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.add_column"><code class="docutils literal notranslate"><span class="pre">RecordBatch.add_column()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.append_column"><code class="docutils literal notranslate"><span class="pre">RecordBatch.append_column()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.cast"><code class="docutils literal notranslate"><span class="pre">RecordBatch.cast()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.column"><code class="docutils literal notranslate"><span class="pre">RecordBatch.column()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.column_names"><code class="docutils literal notranslate"><span class="pre">RecordBatch.column_names</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.columns"><code class="docutils literal notranslate"><span class="pre">RecordBatch.columns</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.copy_to"><code class="docutils literal notranslate"><span class="pre">RecordBatch.copy_to()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.device_type"><code class="docutils literal notranslate"><span class="pre">RecordBatch.device_type</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.drop_columns"><code class="docutils literal notranslate"><span class="pre">RecordBatch.drop_columns()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.drop_null"><code class="docutils literal notranslate"><span class="pre">RecordBatch.drop_null()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.equals"><code class="docutils literal notranslate"><span class="pre">RecordBatch.equals()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.field"><code class="docutils literal notranslate"><span class="pre">RecordBatch.field()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.filter"><code class="docutils literal notranslate"><span class="pre">RecordBatch.filter()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.from_arrays"><code class="docutils literal notranslate"><span class="pre">RecordBatch.from_arrays()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.from_pandas"><code class="docutils literal notranslate"><span class="pre">RecordBatch.from_pandas()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.from_pydict"><code class="docutils literal notranslate"><span class="pre">RecordBatch.from_pydict()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.from_pylist"><code class="docutils literal notranslate"><span class="pre">RecordBatch.from_pylist()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.from_struct_array"><code class="docutils literal notranslate"><span class="pre">RecordBatch.from_struct_array()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.get_total_buffer_size"><code class="docutils literal notranslate"><span class="pre">RecordBatch.get_total_buffer_size()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.is_cpu"><code class="docutils literal notranslate"><span class="pre">RecordBatch.is_cpu</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.itercolumns"><code class="docutils literal notranslate"><span class="pre">RecordBatch.itercolumns()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.nbytes"><code class="docutils literal notranslate"><span class="pre">RecordBatch.nbytes</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.num_columns"><code class="docutils literal notranslate"><span class="pre">RecordBatch.num_columns</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.num_rows"><code class="docutils literal notranslate"><span class="pre">RecordBatch.num_rows</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.remove_column"><code class="docutils literal notranslate"><span class="pre">RecordBatch.remove_column()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.rename_columns"><code class="docutils literal notranslate"><span class="pre">RecordBatch.rename_columns()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.replace_schema_metadata"><code class="docutils literal notranslate"><span class="pre">RecordBatch.replace_schema_metadata()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.schema"><code class="docutils literal notranslate"><span class="pre">RecordBatch.schema</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.select"><code class="docutils literal notranslate"><span class="pre">RecordBatch.select()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.serialize"><code class="docutils literal notranslate"><span class="pre">RecordBatch.serialize()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.set_column"><code class="docutils literal notranslate"><span class="pre">RecordBatch.set_column()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.shape"><code class="docutils literal notranslate"><span class="pre">RecordBatch.shape</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.slice"><code class="docutils literal notranslate"><span class="pre">RecordBatch.slice()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.sort_by"><code class="docutils literal notranslate"><span class="pre">RecordBatch.sort_by()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.take"><code class="docutils literal notranslate"><span class="pre">RecordBatch.take()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.to_pandas"><code class="docutils literal notranslate"><span class="pre">RecordBatch.to_pandas()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.to_pydict"><code class="docutils literal notranslate"><span class="pre">RecordBatch.to_pydict()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.to_pylist"><code class="docutils literal notranslate"><span class="pre">RecordBatch.to_pylist()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.to_string"><code class="docutils literal notranslate"><span class="pre">RecordBatch.to_string()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.to_struct_array"><code class="docutils literal notranslate"><span class="pre">RecordBatch.to_struct_array()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.to_tensor"><code class="docutils literal notranslate"><span class="pre">RecordBatch.to_tensor()</span></code></a></li> |
| <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyarrow.RecordBatch.validate"><code class="docutils literal notranslate"><span class="pre">RecordBatch.validate()</span></code></a></li> |
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